@Article{jai.2020.011541,
AUTHOR = {Chaoyu Deng, Guangfu Zeng, Zhiping Cai, Xiaoqiang Xiao},
TITLE = {A Survey of Knowledge Based Question Answering with Deep Learning},
JOURNAL = {Journal on Artificial Intelligence},
VOLUME = {2},
YEAR = {2020},
NUMBER = {4},
PAGES = {157--166},
URL = {http://www.techscience.com/jai/v2n4/41105},
ISSN = {2579-003X},
ABSTRACT = {The purpose of automated question answering is to let the machine 
understand natural language questions and give accurate answers in the form of 
natural language. This technology requires the machine to store a large amount 
of background knowledge. In recent years, the rapid development of knowledge 
graph has made the knowledge based question answering (KBQA) more and 
more popular. Traditional styles of KBQA methods mainly include semantic 
parsing, information extraction and vector modeling. With the development of 
deep learning, KBQA with deep learning has gradually become the mainstream 
method. This paper introduces the application of deep learning in KBQA mainly 
from the following aspects: the development history of KBQA, KBQA methods 
using deep learning, common datasets used in KBQA, the comparison of various 
methods and the future trend.},
DOI = {10.32604/jai.2020.011541}
}